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PLDI 2020
Mon 15 - Fri 19 June 2020

Several programming languages use garbage collectors (GCs) to automatically manage memory for the programmer. Such collectors must decide when to look for unreachable objects to free, which can have a large performance impact on some applications. In this preliminary work, we propose a design for a learned garbage collector that autonomously learns over time when to perform collections. By using reinforcement learning, our design can incorporate user-defined reward functions, allowing an autonomous garbage collector to learn to optimize the exact metric the user desires (e.g., request latency or queries per second). We conduct an initial experimental study on a prototype, demonstrating that an approach based on tabular Q learning may be promising.

Tue 16 Jun

Displayed time zone: Pacific Time (US & Canada) change

14:00 - 15:00
Formal Methods and Reinforcement LearningMAPL at MAPL live stream
Chair(s): Aws Albarghouthi University of Wisconsin-Madison, USA
Learned Garbage Collection
Lujing Cen MIT CSAIL, Ryan Marcus MIT CSAIL / Intel Labs, Hongzi Mao MIT CSAIL, Justin Gottschlich Intel Labs / Penn, Mohammad Alizadeh MIT CSAIL, Tim Kraska MIT CSAIL
Trustworthy Autonomy through Program Synthesis
Swarat Chaudhuri Rice University